One of the most important attributes of any ML model is its ability to generalize to similar problems, rather than using its parameters to memorize a specific one.
An ML model is expected to not only exhibit low in-sample error, but small out-of sample error as well, computed on data not used during its training. In the context of PL prediction, an ML propagation model should be sufficiently accurate when tested on data collected at different environments or taken at different positions than the ones used to train it. Since \(f\)  is unknown, \(g\)  is only an approximation of \(f\)  computed on a dataset that may also contain noisy samples. Thus, it can be proved that [1]}:
\(\mathcal {E}_{\text{out}}(g) \le \mathcal {E}_{\text{in}}(g) + \Omega (g)\) 
